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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Electrophysiological Signals of Embodiment and MI-BCI Training in VR

Authors: Vagaja, Katarina; Vourvopoulos, Athanasios;

Electrophysiological Signals of Embodiment and MI-BCI Training in VR

Abstract

DATASET DETAILS: Participant demographics: A total of 26 participants were included, consisting of 10 males (mean age 25.4 ± 7.4) and 16 females (mean age 23 ± 3.2). All participants were right-handed, reported normal or corrected-to-normal vision, and had no motor impairments. Three participants had previous experience with BCIs, and five participants used VR more than twice. Participants were randomly assigned to either the embodied group (N=13) or the non-embodied group (N=13), which served as a control. All participants signed an informed consent before participating in the study in accordance with the 1964 Declaration of Helsinki. Experiment Description: A between-subject design was used to investigate the effect of virtual embodiment priming phase on the subsequent motor-imagery training phase in VR. The experiment comprised four main blocks: (1) equipment setup and instructions (45-60 minutes), (2) resting state EEG recording (4 minutes), (3) inducing or breaking the sense of embodiment in VR (5 minutes), and (4) MI training in VR (15 minutes). The entire experiment lasted approximately 90-120 minutes. Directly after block 3, participants answered a questionnaire that measured their subjective sense of embodiment and physical presence. Equipment: A wireless EEG amplifier (LiveAmp; Brain Products GmbH, Gilching, Germany) was used, with 32 active EEG electrodes (+3 ACC) with a sampling rate of 500Hz. In addition, EMG, and Temperature signals (in uV) have been recorded synchronously in a bipolar montage and connected to the EEG amplifier’s AUX input through the Brain Products BIP2AUX adapter. Visual feedback was provided through an Oculus Rift CV1 headset (Reality Labs, formerly Facebook, Inc., CA, USA). Channel Indices: EEG: 1-32 EMG Left (AUX1): 33 EMG Right. (AUX2): 34 Temperature (AUX3): 35 ACC: 36-38 Event codes: Code Description S01 Experiment Start S02 Baseline Start S03 Baseline Stop S04 Start Of Trial S05 Cross On Screen S07 class1, Left hand S08 class2, Right hand S09 Feedback Continuous S10 End of Trial S11 End Of Session S12 Experiment Stop Directory tree: ROOT | +--- GROUP [Control or Embodied] | +---USER # | | +---TASK # | | | +---Resting State | | | | .eeg | | | | .vhdr | | | | .vmrk | | | +---Embodiment | | | | .eeg | | | | .vhdr | | | | .vmrk | | | +---MI | | | | .eeg | | | | .vhdr | | | | .vmrk For demographics and questionnaire data, please contact the authors.

Keywords

Embodiment, EMG, Brain-Computer Interfaces, Virtual Reality, Temperature, Motor Imagery, EEG, Virtual Hand Illusion, Event-Related Desynchronization

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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1
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4